Sentence examples for classification analysis revealed from inspiring English sources

Explorative Bayesian classification analysis revealed that the best predictors of good learning outcomes were wiki-related activities.

Importantly, classification analysis revealed that piriform oscillatory activity conveys olfactory-specific information that can be decoded within 110-518 ms of a sniff, and maximally within the theta frequency band.

Although the same manipulation of temporal structure elicited brain activation level differences of similar magnitude for both music and speech stimuli, multivariate classification analysis revealed distinct spatial patterns of brain responses in the 2 domains.

Data-depth classification analysis revealed a poor categorization among urban, traffic and industrial sites: weather and urban planning factors may cause a general homogeneity of air pollution within cities driving this poor classification.

A multivariate classification analysis revealed a statistically significant group separation between PCA and tAD patients, with 72.7% accuracy (P <.01).

GO Slim Classification analysis revealed a subset of dcCIN genes belong to diverse processes such as DNA damage response, transcription, and cytoplasmic translation.

More detailed analysis based on DAVID gene functional classification analysis reveals that in both types of wounds the most significant changes occur within a group of transcripts that encode structural components of procollagens (Table 8, group 1 in additional file 8 and 9).

PANTHER™ Protein Classification System analysis revealed that genes involved in cell cycle regulation (i.e. RAD21, ORC2, LRPA1, MCM3, FOXO1A, and CCNG1) are significantly over-represented in the set of 144 genes that are differentially expressed in samples from patients with ascending TAA (p-value = 3.4E-3).

PANTHER™ Protein Classification System analysis revealed that this set of genes up-regulated in patients with sporadic TAA was statistically enriched for genes involved in various aspects of DNA metabolism (p-value = 1.1E-6), incluDNAg DNA replication (LOC150580, MCM5, TNKS2), DNA repair (ERCC5, CSNK1A1L) and DNA recombination (SWAP70).

Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers.

GO term classification analysis also revealed significant up-regulation of salivary transcripts associated with transport, cytoskeleton, signaling and protein synthesis machinery (Fig. 7F), indicating that food specific switching may significantly alter molecular architecture of the salivary glands, supporting previous findings (Das et al., 2010).